Machine Learning Risk Models

Zura Kakushadze and Willie Yu

Journal of Risk & Control, 2019, vol. 6, issue 1, 37-64

Abstract: We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions, including statistical risk models, risk models based on fundamental industry classifications, and also those utilizing multilevel clustering based industry classifications.

Keywords: machine learning; risk model; clustering; k-means; statistical risk models; covariance; correlation; variance; cluster number; risk factor; optimization; regression; mean-reversion; factor loadings; principal component; industry classification; quant; trading; dollar-neutral; alpha; signal; backtest (search for similar items in EconPapers)
JEL-codes: G00 G10 G11 G12 G23 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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